ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease

by   Xiaowei Xu, et al.

Congenital heart disease (CHD) is the most common type of birth defect, which occurs 1 in every 110 births in the United States. CHD usually comes with severe variations in heart structure and great artery connections that can be classified into many types. Thus highly specialized domain knowledge and the time-consuming human process is needed to analyze the associated medical images. On the other hand, due to the complexity of CHD and the lack of dataset, little has been explored on the automatic diagnosis (classification) of CHDs. In this paper, we present ImageCHD, the first medical image dataset for CHD classification. ImageCHD contains 110 3D Computed Tomography (CT) images covering most types of CHD, which is of decent size Classification of CHDs requires the identification of large structural changes without any local tissue changes, with limited data. It is an example of a larger class of problems that are quite difficult for current machine-learning-based vision methods to solve. To demonstrate this, we further present a baseline framework for the automatic classification of CHD, based on a state-of-the-art CHD segmentation method. Experimental results show that the baseline framework can only achieve a classification accuracy of 82.0% under a selective prediction scheme with 88.4% coverage, leaving big room for further improvement. We hope that ImageCHD can stimulate further research and lead to innovative and generic solutions that would have an impact in multiple domains. Our dataset is released to the public compared with existing medical imaging datasets.


page 3

page 6


ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection

Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascula...

EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis

Patent foramen ovale (PFO) is a potential separation between the septum,...

Accurate Congenital Heart Disease Model Generation for 3D Printing

3D printing has been widely adopted for clinical decision making and int...

Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images

The evaluation of infectious disease processes on radiologic images is a...

Accurate Congenital Heart Disease ModelGeneration for 3D Printing

3D printing has been widely adopted for clinical decision making and int...

Automated recognition of the pericardium contour on processed CT images using genetic algorithms

This work proposes the use of Genetic Algorithms (GA) in tracing and rec...

SHREC 2021: Classification in cryo-electron tomograms

Cryo-electron tomography (cryo-ET) is an imaging technique that allows t...

Please sign up or login with your details

Forgot password? Click here to reset